PEMODELAN PRODUKSI PADI DI INDONESIA DENGAN PENDEKATAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION

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dc.contributor.authorMuslihah, Ririn Ridania
dc.contributor.supervisorEfendi, Rustam
dc.date.accessioned2021-10-19T02:00:42Z
dc.date.available2021-10-19T02:00:42Z
dc.date.issued2021-03
dc.description.abstractThis paper discusses about rice production modeling in Indonesia with Mixed Geographically Weighted Regression (MGWR) approach. The MGWR model is a combination of multiple linear regression models and Geographically Weighted Regression (GWR), which model variables that are global and local. The estimated parameters of the MGWR model are obtained using the Weighted Least Square (WLS) method. The optimum bandwidth is selected by using the Cross Validation (CV) method. Rice production in Indonesia is influenced by factors such as harvest area, rainfall, fertilizer, and farmers. MGWR model produces variables of local nature is the area of harvest, while the variables that are global are rainfall, fertilizer and farmers. MGWR model is a best model used to model rice production data in Indonesia based on Akaike Information Criteria Corrected valueen_US
dc.description.sponsorshipJurusan Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Riauen_US
dc.identifier.otherwahyu sari yeni
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/10259
dc.language.isoenen_US
dc.subjectRice productionen_US
dc.subjectMixed Geographically Weighted Regressionen_US
dc.subjectWeighted Least Squareen_US
dc.subjectCross Validationen_US
dc.subjectAkaike Information Criterion Correcteden_US
dc.titlePEMODELAN PRODUKSI PADI DI INDONESIA DENGAN PENDEKATAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSIONen_US
dc.typeArticleen_US

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